Classification of chaotic time series with deep learning

Classification of chaotic time series with deep learning

Classification of chaotic time series with deep learning 150 150 UKAEA Opendata
UKAEA-CCFE-PR(19)66

Classification of chaotic time series with deep learning

We use deep neural networks to classify time series generated by discrete and continuous dynamical systems based on their chaotic behavior. Our approach to circumvent the lack of precise models for some of the most challenging real-life applications is to train different neural networks on a data set from a dynamical system with a basic or low-dimensional phase space and then use these networks to classify time series of a dynamical system with more intricate or high-dimensional phase space. We illustrate this extrapolation approach using the logistic map, the sine-circle map, the Lorenz system, and the Kuramoto–Sivashinsky equation. We observe that the proposed convolutional neural network with large kernel size outperforms state-of-the-art neural networks for time series classification and is able to classify time series as chaotic or non-chaotic with high accuracy.

Collection:
Journals
Journal:
Physica D
Publisher:
Elsevier
Published date:
04/12/2019